Companies provide services that users access using client devices. For example, a user may view a video in a media player. The companies often seek to improve their service by analyzing events that occur while the users are using their client devices. For example, while viewing the video, the user performs different actions, such as seeking to different times in the video, stopping the video, hovering over icons, etc. Web requests are generated to document the actions taken at the client devices (also referred to as “beacons”). For example, when a user's browser requests information from a website, a server may aggregate information, such as the IP address of the computer being used; the time the material was viewed; the type of browser that was used, the type of action taken by the user, etc. The beacons are logged and aggregated for the company.
The beacons include information that is in an unstructured format. The unstructured format is not in a pre-defined data model that a company can easily store in a structured database. For example, many analysis applications are keyed to retrieve data in fields in a structured database. The beacons do not include data that can easily be stored in the correct fields. Thus, if a company is going to analyze the information in the beacons, the company needs to transform the unstructured data into structured data. The structured data organizes the data in a format desired by the company where the company can then analyze the structured data.
The company may want to aggregate the structured data that is received into certain reports or into other tables of a database. For example, an aggregation may be performed hourly, daily, or weekly. For example, a user may want to know how many views of a video occur every hour. This would require a separate query for every hour block. Since a query needs to be run every hour, a user must manually write different queries for every hour. This becomes a tedious process to have to write so many queries.